Key result
An end-to-end edge-enabled machine learning-based VLSI architecture achieved 92.37% accuracy for class-oriented classification of atrial fibrillation while consuming 11.098 µW at 25 kHz.
Why the study?
Manual diagnosis of AF from ECG signals is challenging due to complex and varied characteristic changes, creating a need for automated low-power classification suitable for wearable devices.
The proposed low-power DNN-based VLSI architecture achieves high accuracy for AF detection, making it suitable for wearable devices.
May enable wearable AF detection; leaves open prospective clinical validation before practice adoption.
Atrial fibrillation (AF) is a recurrent and life-threatening disease leading to rapid growth in the mortality rate due to cardiac abnormalities. It is challenging to manually diagnose AF using electrocardiogram (ECG) signals due to complex and varied changes in its characteristics. In this article, for the first time, an end-to-end edge-enabled machine learning-based VLSI architecture is proposed to classify ECG excerpts having AF from normal beats. Researchers have found that abnormal atrial activity is confined to the low-frequency range through the decades. Therefore, in the proposed work, this frequency band is directly analyzed for AF detection, which has not previously been discussed. The proposed architecture is implemented using 180-nm bulk CMOS technology consuming11.098~μ Wat25~ kHzand exhibits an accuracy of 92.37% for class-oriented classification and 81.60% for subject-oriented classification. The low-power realization of the proposed design, as compared to the state-of-the-art methods, makes it suitable to be used for wearable devices.
No takes yet. Share an insight, caveat, or question.
Parmar et al. (2023) studied Atrial fibrillation. DNN-Based Low-Power VLSI Architecture vs. state-of-the-art methods was evaluated on Classification of ECG excerpts having AF from normal beats. An end-to-end edge-enabled machine learning-based VLSI architecture achieved 92.37% accuracy for class-oriented classification of atrial fibrillation while consuming 11.098 µW at 25 kHz.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: